posted on 2023-12-28, 01:04authored byDina Kussainova, Athanassios Z. Panagiotopoulos
We developed a first-principles machine learning model
for the
reactive vapor–liquid phase behavior of molten Li2CO3. The model was trained on ab initio electronic density functional theory data using the Deep Potential
(DP) methodology, and its accuracy was evaluated by comparing model
predictions of density and viscosity to experimental measurements.
Direct coexistence simulations with the DP model over time scales
of tens of nanoseconds were used to observe equilibrium dissociation
of Li2CO3 into CO2 residing primarily
in the vapor phase and Li2O which remains dissolved in
the liquid. The simulations covered a range of temperatures, overall
system sizes, and vapor-to-liquid volume ratios. Results were analyzed
in terms of the observed chemical composition of the liquid and vapor
phases, product structure, and CO2 partial pressures. In
addition, we calculated equilibrium constants for the dissociation
reaction by assuming ideal-solution behavior for the liquid. As expected
on the basis of thermodynamic arguments and prior experiments for
this system, the observed partial pressure of CO2 in the
gas phase depends on both the temperature and the ratio of vapor to
liquid volumes, while the calculated equilibrium constants only depend
on temperature. DP model predictions for the equilibrium constant
of the reaction are generally consistent with the available experimental
measurements. The present study establishes the validity of the DP
methodology for the description of reactive, multiphase equilibria
from first principles, with possible applications to many other systems
of scientific and technological interest even in the absence of relevant
experimental measurements.